资产类别内的套息因子
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 资产类别内的套息因子 in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 资产类别内的套息因子
# Detected pattern: Custom / hybrid
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
def Rebalance(self):
# Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
导出代码使用目标平台的原生类与库。请在第三方 IDE 中安装依赖后运行；实盘前请自行验证。
学术论文
策略概要
该策略涵盖13种股指期货、19种货币远期合约、23种商品期货和10种政府债券合约，重点关注四种类型的套利：股票套利、货币套利、商品套利和债券套利。
货币套利：做多高利率货币，做空低利率货币。
股票套利：预期股息收益率减去无风险利率。
商品套利：便利收益超过存储成本，源自近期和长期到期期货价格。
债券套利：到期收益率超过短期无风险利率。
在每个子领域中，做多高套利工具，做空低套利工具，权重基于套利排名。投资组合每月进行再平衡。多元化的套利策略结合了所有资产类别的等波动率加权回报，优化了跨市场对套利交易潜力的敞口。
II. 策略合理性
学术研究表明，套利效应存在于全球股票、债券、商品和货币中。资产的“套利”代表其在假设价格不变情况下的预期回报，提供了一种无模型、直接可观测的预期回报衡量标准。与需要模型估计的价格升值不同，套利与主要资产类别的预期回报可靠相关，随时间和资产而变化，使其成为回报变异性的预测指标。然而，多头套利头寸的回报溢价可能补偿在全球经济衰退和流动性紧缩期间遭受重大损失的风险敞口，突显其在波动经济条件下的风险回报性质。